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Article Dans Une Revue ICES Journal of Marine Science Année : 2023

Machine learning in marine ecology: an overview of techniques and applications

Dimitris Politikos
Rainer Kiko
Madiop Lo
  • Fonction : Auteur
Klas Ove Möller
Ahmet Pala
  • Fonction : Auteur
Pia Schuchert
  • Fonction : Auteur
Vahid Seydi
  • Fonction : Auteur
Sebastian Villasante
  • Fonction : Auteur
Ketil Malde

Résumé

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.
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hal-04284704 , version 1 (14-11-2023)

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Peter Rubbens, Stephanie Brodie, Tristan Cordier, Diogo Destro Barcellos, Paul Devos, et al.. Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science, 2023, 80 (7), pp.1829-1853. ⟨10.1093/icesjms/fsad100⟩. ⟨hal-04284704⟩
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